Abstract
Chest radiography is widely used in annual medical screening to check whether lungs are healthy or not. Therefore it would be desirable to develop an intelligent system to help clinicians automatically detect potential abnormalities in chest X-ray images. Here with only healthy X-ray images, we propose a new abnormality detection approach based on an autoencoder which outputs not only the reconstructed normal version of the input image but also a pixel-wise uncertainty prediction. Higher uncertainty often appears at normal region boundaries with relatively larger reconstruction errors, but not at potential abnormal regions in the lung area. Therefore the normalized reconstruction error by the uncertainty provides a natural measurement for abnormality detection in images. Experiments on two chest X-ray datasets show the state-of-the-art performance by the proposed approach.
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References
Alaverdyan, Z., Jung, J., Bouet, R., Lartizien, C.: Regularized siamese neural network for unsupervised outlier detection on brain multiparametric magnetic resonance imaging: application to epilepsy lesion screening. Med. Image Anal. 60, 101618 (2020)
Baur, C., Wiestler, B., Albarqouni, S., Navab, N.: Fusing unsupervised and supervised deep learning for white matter lesion segmentation. In: International Conference on Medical Imaging with Deep Learning, pp. 63–72 (2019)
Chen, X., Konukoglu, E.: Unsupervised detection of lesions in brain MRI using constrained adversarial auto-encoders. In: International Conference on Medical Imaging with Deep Learning (2018)
Chen, X., Pawlowski, N., Glocker, B., Konukoglu, E.: Unsupervised lesion detection with locally Gaussian approximation. In: Suk, H.-I., Liu, M., Yan, P., Lian, C. (eds.) MLMI 2019. LNCS, vol. 11861, pp. 355–363. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32692-0_41
Diederik, P.K., Welling, M., et al.: Auto-encoding variational bayes. In: Proceedings of the International Conference on Learning Representations, vol. 1 (2014)
Dorta, G., Vicente, S., Agapito, L., Campbell, N.D., Simpson, I.: Structured uncertainty prediction networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5477–5485 (2018)
Gong, D., et al.: Memorizing normality to detect anomaly: memory-augmented deep autoencoder for unsupervised anomaly detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1705–1714 (2019)
Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)
He, Y., Zhu, C., Wang, J., Savvides, M., Zhang, X.: Bounding box regression with uncertainty for accurate object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2888–2897 (2019)
Kendall, A., Gal, Y.: What uncertainties do we need in Bayesian deep learning for computer vision? In: Advances in Neural Information Processing Systems, pp. 5574–5584 (2017)
Mourão-Miranda, J., et al.: Patient classification as an outlier detection problem: an application of the one-class support vector machine. NeuroImage 58(3), 793–804 (2011)
Sabokrou, M., Khalooei, M., Fathy, M., Adeli, E.: Adversarially learned one-class classifier for novelty detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3379–3388 (2018)
Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-AnoGAN: fast unsupervised anomaly detection with generative adversarial networks. Med. Image Anal. 54, 30–44 (2019)
Schlegl, T., Seeböck, P., Waldstein, S.M., Schmidt-Erfurth, U., Langs, G.: Unsupervised anomaly detection with generative adversarial networks to guide marker discovery. In: Niethammer, M., et al. (eds.) IPMI 2017. LNCS, vol. 10265, pp. 146–157. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59050-9_12
Schölkopf, B., Williamson, R.C., Smola, A.J., Shawe-Taylor, J., Platt, J.C.: Support vector method for novelty detection. In: Advances in Neural Information Processing Systems, pp. 582–588 (2000)
Seeböck, P., et al.: Unsupervised identification of disease marker candidates in retinal OCT imaging data. IEEE Trans. Med. Imaging 38(4), 1037–1047 (2018)
Sidibe, D., et al.: An anomaly detection approach for the identification of DME patients using spectral domain optical coherence tomography images. Comput. Methods Programs Biomed. 139, 109–117 (2017)
Tang, Y.X., Tang, Y.B., Han, M., Xiao, J., Summers, R.M.: Abnormal chest x-ray identification with generative adversarial one-class classifier. In: IEEE International Symposium on Biomedical Imaging, pp. 1358–1361 (2019)
Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., Summers, R.M.: ChestX-ray8: hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2097–2106 (2017)
Ziegler, G., Ridgway, G.R., Dahnke, R., Gaser, C., Initiative, A.D.N., et al.: Individualized Gaussian process-based prediction and detection of local and global gray matter abnormalities in elderly subjects. NeuroImage 97, 333–348 (2014)
Acknowledgement
This work is supported in part by the National Key Research and Development Program (grant No. 2018YFC1315402), the Guangdong Key Research and Development Program (grant No. 2019B020228001), the National Natural Science Foundation of China (grant No. U1811461), the Guangzhou Science and Technology Program (grant No. 201904010260) and the National Key R&D Program of China (grant No. 2017YFB0802500).
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Mao, Y., Xue, FF., Wang, R., Zhang, J., Zheng, WS., Liu, H. (2020). Abnormality Detection in Chest X-Ray Images Using Uncertainty Prediction Autoencoders. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12266. Springer, Cham. https://doi.org/10.1007/978-3-030-59725-2_51
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DOI: https://doi.org/10.1007/978-3-030-59725-2_51
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